Next Article in Journal
Bearing Fault Diagnosis with a Feature Fusion Method Based on an Ensemble Convolutional Neural Network and Deep Neural Network
Previous Article in Journal
Phenotyping of Plant Biomass and Performance Traits Using Remote Sensing Techniques in Pea (Pisum sativum, L.)
Open AccessArticle

Design and Analysis of a True Random Number Generator Based on GSR Signals for Body Sensor Networks

by 1,*,†,‡, 2,‡, 1,‡ and 3,‡
1
Department of Computer Science, University Carlos III of Madrid, 28911 Leganes, Spain
2
Department of Electronic Technology, University Carlos III of Madrid, 28911 Leganes, Spain
3
Higher Colleges of Technology, Abu Dhabi Women’s College, Abu Dhabi 41012, United Arab Emirates
*
Author to whom correspondence should be addressed.
Current address: Department of Computer Science, University Carlos III of Madrid, Avda. de la Universidad, 30, 28911 Leganés, Madrid, Spain.
These authors contributed equally to this work.
Sensors 2019, 19(9), 2033; https://doi.org/10.3390/s19092033
Received: 24 March 2019 / Revised: 21 April 2019 / Accepted: 23 April 2019 / Published: 30 April 2019
(This article belongs to the Section Internet of Things)
Today, medical equipment or general-purpose devices such as smart-watches or smart-textiles can acquire a person’s vital signs. Regardless of the type of device and its purpose, they are all equipped with one or more sensors and often have wireless connectivity. Due to the transmission of sensitive data through the insecure radio channel and the need to ensure exclusive access to authorised entities, security mechanisms and cryptographic primitives must be incorporated onboard these devices. Random number generators are one such necessary cryptographic primitive. Motivated by this, we propose a True Random Number Generator (TRNG) that makes use of the GSR signal measured by a sensor on the body. After an exhaustive analysis of both the entropy source and the randomness of the output, we can conclude that the output generated by the proposed TRNG behaves as that produced by a random variable. Besides, and in comparison with the previous proposals, the performance offered is much higher than that of the earlier works. View Full-Text
Keywords: Galvanic Skin Response (GSR); entropy; randomness; Random Number Generators (RNG); Hilbert transform Galvanic Skin Response (GSR); entropy; randomness; Random Number Generators (RNG); Hilbert transform
Show Figures

Graphical abstract

MDPI and ACS Style

Camara, C.; Martín, H.; Peris-Lopez, P.; Aldalaien, M. Design and Analysis of a True Random Number Generator Based on GSR Signals for Body Sensor Networks. Sensors 2019, 19, 2033. https://doi.org/10.3390/s19092033

AMA Style

Camara C, Martín H, Peris-Lopez P, Aldalaien M. Design and Analysis of a True Random Number Generator Based on GSR Signals for Body Sensor Networks. Sensors. 2019; 19(9):2033. https://doi.org/10.3390/s19092033

Chicago/Turabian Style

Camara, Carmen; Martín, Honorio; Peris-Lopez, Pedro; Aldalaien, Muawya. 2019. "Design and Analysis of a True Random Number Generator Based on GSR Signals for Body Sensor Networks" Sensors 19, no. 9: 2033. https://doi.org/10.3390/s19092033

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop